SoroushVahidi/scidocs huggingface dataset:
Improved seam carving for video retargeting
(2008)

Video, like images, should support content aware resizing. We present video retargeting using an improved seam carving operator. Instead of removing 1D seams from 2D images we remove 2D seam manifolds from 3D space-time volumes. To achieve this we replace the dynamic programming method of seam carving with graph cuts that are suitable for 3D volumes. In the new formulation, a seam is given by a minimal cut in the graph and we show how to construct a graph such that the resulting cut is a valid seam. That is, the cut is monotonic and connected. In addition, we present a novel energy criterion that improves the visual quality of the retargeted images and videos. The original seam carving operator is focused on removing seams with the least amount of energy, ignoring energy that is introduced into the images and video by applying the operator. To counter this, the new criterion is looking forward in time - removing seams that introduce the least amount of energy into the retargeted result. We show how to encode the improved criterion into graph cuts (for images and video) as well as dynamic programming (for images). We apply our technique to images and videos and present results of various applications.
FlowFence: Practical Data Protection for Emerging IoT Application Frameworks
(2016)

Emerging IoT programming frameworks enable building apps that compute on sensitive data produced by smart homes and wearables. However, these frameworks only support permission-based access control on sensitive data, which is ineffective at controlling how apps use data once they gain access. To address this limitation, we present FlowFence, a system that requires consumers of sensitive data to declare their intended data flow patterns, which it enforces with low overhead, while blocking all other undeclared flows. FlowFence achieves this by explicitly embedding data flows and the related control flows within app structure. Developers use FlowFence support to split their apps into two components: (1) A set of Quarantined Modules that operate on sensitive data in sandboxes, and (2) Code that does not operate on sensitive data but orchestrates execution by chaining Quarantined Modules together via taint-tracked opaque handles—references to data that can only be dereferenced inside sandboxes. We studied three existing IoT frameworks to derive key functionality goals for FlowFence, and we then ported three existing IoT apps. Securing these apps using FlowFence resulted in an average increase in size from 232 lines to 332 lines of source code. Performance results on ported apps indicate that FlowFence is practical: A face-recognition based doorcontroller app incurred a 4.9% latency overhead to recognize a face and unlock a door.
Unauthorized origin crossing on mobile platforms: threats and mitigation
(2013)

With the progress in mobile computing, web services are increasingly delivered to their users through mobile apps, instead of web browsers. However, unlike the browser, which enforces origin-based security policies to mediate the interactions between the web content from different sources, today's mobile OSes do not have a comparable security mechanism to control the cross-origin communications between apps, as well as those between an app and the web. As a result, a mobile user's sensitive web resources could be exposed to the harms from a malicious origin. In this paper, we report the first systematic study on this mobile cross-origin risk. Our study inspects the main cross-origin channels on Android and iOS, including intent, scheme and web-accessing utility classes, and further analyzes the ways popular web services (e.g., Facebook, Dropbox, etc.) and their apps utilize those channels to serve other apps. The research shows that lack of origin-based protection opens the door to a wide spectrum of cross-origin attacks. These attacks are unique to mobile platforms, and their consequences are serious: for example, using carefully designed techniques for mobile cross-site scripting and request forgery, an unauthorized party can obtain a mobile user's Facebook/Dropbox authentication credentials and record her text input. We report our findings to related software vendors, who all acknowledged their importance. To address this threat, we designed an origin-based protection mechanism, called Morbs, for mobile OSes. Morbs labels every message with its origin information, lets developers easily specify security policies, and enforce the policies on the mobile channels based on origins. Our evaluation demonstrates the effectiveness of our new technique in defeating unauthorized origin crossing, its efficiency and the convenience for the developers to use such protection.
Aurasium: Practical Policy Enforcement for Android Applications
(2012)

The increasing popularity of Google’s mobile platform Android makes it the prime target of the latest surge in mobile malware. Most research on enhancing the platform’s security and privacy controls requires extensive modification to the operating system, which has significant usability issues and hinders efforts for widespread adoption. We develop a novel solution called Aurasium that bypasses the need to modify the Android OS while providing much of the security and privacy that users desire. We automatically repackage arbitrary applications to attach user-level sandboxing and policy enforcement code, which closely watches the application’s behavior for security and privacy violations such as attempts to retrieve a user’s sensitive information, send SMS covertly to premium numbers, or access malicious IP addresses. Aurasium can also detect and prevent cases of privilege escalation attacks. Experiments show that we can apply this solution to a large sample of benign and malicious applications with a near 100 percent success rate, without significant performance and space overhead. Aurasium has been tested on three versions of the Android OS, and is freely available.
Lessons learned from the deployment of a smartphone-based access-control system
(2007)

Grey is a smartphone-based system by which a user can exercise her authority to gain access to rooms in our university building, and by which she can delegate that authority to other users. We present findings from a trial of Grey, with emphasis on how common usability principles manifest themselves in a smartphone-based security application. In particular, we demonstrate aspects of the system that gave rise to failures, misunderstandings, misperceptions, and unintended uses; network effects and new flexibility enabled by Grey; and the implications of these for user behavior. We argue that the manner in which usability principles emerged in the context of Grey can inform the design of other such applications.
NL-InSAR: Nonlocal Interferogram Estimation
(2011)

Interferometric synthetic aperture radar (SAR) data provide reflectivity, interferometric phase, and coherence images, which are paramount to scene interpretation or low-level processing tasks such as segmentation and 3-D reconstruction. These images are estimated in practice from a Hermitian product on local windows. These windows lead to biases and resolution losses due to the local heterogeneity caused by edges and textures. This paper proposes a nonlocal approach for the joint estimation of the reflectivity, the interferometric phase, and the coherence images from an interferometric pair of coregistered single-look complex (SLC) SAR images. Nonlocal techniques are known to efficiently reduce noise while preserving structures by performing the weighted averaging of similar pixels. Two pixels are considered similar if the surrounding image patches are “resembling.” Patch similarity is usually defined as the Euclidean distance between the vectors of graylevels. In this paper, a statistically grounded patch-similarity criterion suitable to SLC images is derived. A weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way. Weights are defined from the intensity and interferometric phase and are iteratively refined based both on the similarity between noisy patches and on the similarity of patches from the previous estimate. The efficiency of this new interferogram construction technique is illustrated both qualitatively and quantitatively on synthetic and true data.
Pattern Recognition: Possible Research Areas and Issues
(2014)

Pattern recognition is a tough problem for computers, although humans are wired for it. Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval. This paper reviews possible application areas of Pattern recognition. Author covers various sub-disciplines of pattern recognition based on learning methods, such as supervised, unsupervised, semi-supervised learning and key research areas such as grammar induction. Novel solutions to these possible problems could be well deployed for character recognition, speech analysis, man and machine diagnostics, person identification, industrial inspection and so on. The paper concludes with brief discussion on open issues that need to be addressed by future researchers.
A Dataset for Web-Scale Knowledge Base Population
(2018)

For many domains, structured knowledge is in short supply, while unstructured text is plentiful. Knowledge Base Population (KBP) is the task of building or extending a knowledge base from text, and systems for KBP have grown in capability and scope. However, existing datasets for KBP are all limited by multiple issues: small in size, not open or accessible, only capable of benchmarking a fraction of the KBP process, or only suitable for extracting knowledge from title-oriented documents (documents that describe a particular entity, such as Wikipedia pages). We introduce and release CC-DBP, a web-scale dataset for training and benchmarking KBP systems. The dataset is based on Common Crawl as the corpus and DBpedia as the target knowledge base. Critically, by releasing the tools to build the dataset, we enable the dataset to remain current as new crawls and DBpedia dumps are released. Also, the modularity of the released tool set resolves a crucial tension between the ease that a dataset can be used for a particular subtask in KBP and the number of different subtasks it can be used to train or benchmark.
WebGLStudio: a pipeline for WebGL scene creation
(2013)

We present WebGLStudio - a pipeline for the creation and editing of high-quality 3D scenes using WebGL. The pipeline features a 3D Scene-Graph rendering engine; an interactive scene editor allowing detailed setup and configuration of assets in real-time; a web-based asset manager; and a stand-alone rendering module ready to embedded in target applications. We further present a series of implementational details discovered to overcome limitations of the web browser context to enable realistic rendering and performance. The principal contribution of this work to the graphics community is the methodology used to take advantage of several unique aspects of Javascript when used as a 3D programming language; and the demonstration of possibilities involved in real-time editing of the parameters of materials, shaders and post-processing effects, in order for the user/artist to create a 3D scene as desired.
BOSCH RARE SOUND EVENTS DETECTION SYSTEMS FOR DCASE 2017 CHALLENGE
(2017)

In this report, we describe three systems designed at BOSCH for rare audio sound events detection task of DCASE 2017 challenge. The first system is an end-to-end audio event segmentation using embeddings based on deep convolutional neural network (DCNN) and deep recurrent neural network (DRNN) trained on Mel-filter banks and spectogram features. Both system 2 and 3 contain two parts: audio event tagging and audio event segmentation. Audio event tagging selects the positive audio recordings (containing audio events), which are later processed by the audio segmentation part. Feature selection method has been deployed to select a subset of features in both systems. System 2 employs Dilated convolutional neural network on the selected features for audio tagging, and an audio-codebook approach to convert audio features to audio vectors (Audio2vec system) which are then passed to an LSTM network for audio events boundary prediction. System 3 is based on multiple instance learning problem using variational auto encoder (VAE) to perform audio event tagging and segmentation. Similar to system 2, here a LSTM network is used for audio segmentation. Finally, we have utilized models based on score-fusion among different systems to improve the final results.
Semantic homophily in online communication: Evidence from Twitter
(2017)

People are observed to assortatively connect on a set of traits. This phenomenon, termed assortative mixing or sometimes homophily, can be quantified through assortativity coefficient in social networks. Uncovering the exact causes of strong assortative mixing found in social networks has been a research challenge. Among the main suggested causes from sociology are the tendency of similar individuals to connect (often itself referred as homophily) and the social influence among already connected individuals. Distinguishing between these tendencies and other plausible causes and quantifying their contribution to the amount of assortative mixing has been a difficult task, and proven not even possible from observational data. However, another task of similar importance to researchers and in practice can be tackled, as we present here: understanding the exact mechanisms of interplay between these tendencies and the underlying social network structure. Namely, in addition to the mentioned assortativity coefficient, there are several other static and temporal network properties and substructures that can be linked to the tendencies of homophily and social influence in the social network and we herein investigate those. Concretely, we tackle a computer-mediated communication network (based on Twitter mentions) and a particular type of assortative mixing that can be inferred from the semantic features of communication content that we term semantic homophily. Our work, to the best of our knowledge, is the first to offer an in-depth analysis on semantic homophily in a communication network and the interplay between them. We quantify diverse levels of semantic homophily, identify the semantic aspects that are the drivers of observed homophily, show insights in its temporal evolution and finally, we present its intricate interplay with the communication network on Twitter. By analyzing these mechanisms we increase understanding on what are the semantic aspects that shape and how they shape the human computer-mediated communication. In addition, our analysis framework presented on this concrete case can be easily adapted, extended and applied on other type of social networks and for different types of homophily.
An Iterative Algorithm for Camera Pose Estimation with Lie Group Representation
(2017)

For solving camera pose problem, a novel accurate and fast pose estimation algorithm based on Lie group representation is proposed. Firstly, object function based on total space error of all feature points is built. Secondly, object function is linearized based on Lie group representation. The Jacobian matrix of camera pose is deduced and pose parameters are updated. Finally, the computation efficiency of algorithm is promoted by using Kronecker product to optimize the iteration process. Simulation and real experiments show that the proposed algorithm is able to obtain camera pose with high precision, but consumes less time compared with the existing algorithms. The proposed algorithm is capable of estimating camera pose with high precision and efficiency.
Accurate human motion capture in large areas by combining IMU- and laser-based people tracking
(2011)

A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial sensors that allow for free and large-scale motion but suffer from drift in the pose estimate. This paper presents a novel tracking approach that aims to provide globally aligned full body posture estimates by combining a mobile robot and an inertial motion capture system. In our approach, a mobile robot equipped with a laser scanner is used to anchor the pose estimates of a person given a map of the environment. It uses a particle filter to globally localize a person wearing a motion capture suit and to robustly track the person's position. To obtain a smooth and globally aligned trajectory of the person, we solve a least squares optimization problem formulated from the motion capture suite and tracking data. Our approach has been implemented on a real robot and exhaustively tested. As the experimental evaluation shows, our system is able to provide locally precise and globally aligned estimates of the person's full body posture.
Predicting the severity of a reported bug
(2010)

The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65–0.75 with Mozilla and Eclipse; 0.70–0.85 in the case of GNOME).
BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing
(2016)

Basic Linear Algebra Subprograms (BLAS) are a set of low level linear algebra kernels widely adopted by applications involved with the deep learning and scientific computing. The massive and economic computing power brought forth by the emerging GPU architectures drives interest in implementation of compute-intensive level 3 BLAS on multi-GPU systems. In this paper, we investigate existing multi-GPU level 3 BLAS and present that 1) issues, such as the improper load balancing, inefficient communication, insufficient GPU stream level concurrency and data caching, impede current implementations from fully harnessing heterogeneous computing resources; 2) and the inter-GPU Peer-to-Peer(P2P) communication remains unexplored. We then present BLASX: a highly optimized multi-GPU level-3 BLAS. We adopt the concepts of algorithms-by-tiles treating a matrix tile as the basic data unit and operations on tiles as the basic task. Tasks are guided with a dynamic asynchronous runtime, which is cache and locality aware. The communication cost under BLASX becomes trivial as it perfectly overlaps communication and computation across multiple streams during asynchronous task progression. It also takes the current tile cache scheme one step further by proposing an innovative 2-level hierarchical tile cache, taking advantage of inter-GPU P2P communication. As a result, linear speedup is observable with BLASX under multi-GPU configurations; and the extensive benchmarks demonstrate that BLASX consistently outperforms the related leading industrial and academic implementations such as cuBLAS-XT, SuperMatrix, MAGMA.
Self-Attentional Acoustic Models
(2018)

Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities. These models have recently shown promising results for modeling discrete sequences, but they are non-trivial to apply to acoustic modeling due to computational and modeling issues. In this paper, we apply self-attention to acoustic modeling, proposing several improvements to mitigate these issues: First, self-attention memory grows quadratically in the sequence length, which we address through a downsampling technique. Second, we find that previous approaches to incorporate position information into the model are unsuitable and explore other representations and hybrid models to this end. Third, to stress the importance of local context in the acoustic signal, we propose a Gaussian biasing approach that allows explicit control over the context range. Experiments find that our model approaches a strong baseline based on LSTMs with networkin-network connections while being much faster to compute. Besides speed, we find that interpretability is a strength of selfattentional acoustic models, and demonstrate that self-attention heads learn a linguistically plausible division of labor.
Peginterferon alfa-2a plus ribavirin for chronic hepatitis C virus infection.
(2002)

BACKGROUND
Treatment with peginterferon alfa-2a alone produces significantly higher sustained virologic responses than treatment with interferon alfa-2a alone in patients with chronic hepatitis C virus (HCV) infection. We compared the efficacy and safety of peginterferon alfa-2a plus ribavirin, interferon alfa-2b plus ribavirin, and peginterferon alfa-2a alone in the initial treatment of chronic hepatitis C.


METHODS
A total of 1121 patients were randomly assigned to treatment and received at least one dose of study medication, consisting of 180 microg of peginterferon alfa-2a once weekly plus daily ribavirin (1000 or 1200 mg, depending on body weight), weekly peginterferon alfa-2a plus daily placebo, or 3 million units of interferon alfa-2b thrice weekly plus daily ribavirin for 48 weeks.


RESULTS
A significantly higher proportion of patients who received peginterferon alfa-2a plus ribavirin had a sustained virologic response (defined as the absence of detectable HCV RNA 24 weeks after cessation of therapy) than of patients who received interferon alfa-2b plus ribavirin (56 percent vs. 44 percent, P<0.001) or peginterferon alfa-2a alone (56 percent vs. 29 percent, P<0.001). The proportions of patients with HCV genotype 1 who had sustained virologic responses were 46 percent, 36 percent, and 21 percent, respectively, for the three regimens. Among patients with HCV genotype 1 and high base-line levels of HCV RNA, the proportions of those with sustained virologic responses were 41 percent, 33 percent, and 13 percent, respectively. The overall safety profiles of the three treatment regimens were similar; the incidence of influenza-like symptoms and depression was lower in the groups receiving peginterferon alfa-2a than in the group receiving interferon alfa-2b plus ribavirin.


CONCLUSIONS
In patients with chronic hepatitis C, once-weekly peginterferon alfa-2a plus ribavirin was tolerated as well as interferon alfa-2b plus ribavirin and produced significant improvements in the rate of sustained virologic response, as compared with interferon alfa-2b plus ribavirin or peginterferon alfa-2a alone.
The New Algorithms of Weighted Association Rules based on Apriori and FP-Growth Methods
(2014)

In order to improve the frequent itemsets generated layer-wise efficiency, the paper uses the Apriori property to reduce the search space. FP-grow algorithm for mining frequent pattern steps mainly is divided into two steps: FP-tree and FP-tree to construct a recursive mining. Algorithm FP-Growth is to avoid the high cost of candidate itemsets generation, fewer, more efficient scanning. The paper puts forward the new algorithms of weighted association rules based on Apriori and FP-Growth methods. In the same support, this method is the most effective and stable maximum frequent itemsets mining capacity and minimum execution time. Through theoretical analysis and experimental simulation of the performance of the algorithm is discussed, it is proved that the algorithm is feasible and effective.